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Cold-inducible RNA binding promotes breast cancer cell malignancy by regulating

Cystatin C levels

Alberto Indacochea1,2, Santiago Guerrero1, 5*, Macarena Ureña2, Ferrán Araujo2, Olga Coll1,

Matilde E. LLeonart2,3* and Fátima Gebauer1,4*

(1) Regulation, Stem Cells and Cancer Programme, Centre for Genomic Regulation

(CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Spain

(2) Biomedical Research in Cancer Stem Cells, Vall d´Hebron Research Institute (VHIR),

Passeig Vall d´Hebron 119-129, 08035 Barcelona, Spain

(3) Spanish Biomedical Research Network Centre in Oncology, CIBERONC, Spain

(4) Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain

(5) Current address: Centro de Investigación Genética y Genómica, Facultad de Ciencias de la

Salud Eugenio Espejo, Universidad UTE, Av. Mariscal Sucre and Mariana de Jesús, 170129

Quito, Ecuador

Running title: CIRBP targets in breast cancer

Keywords: CIRBP, CST3, breast cancer

(*) Co-corresponding: [email protected] [email protected] [email protected]

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Abstract

Cold-inducible RNA binding protein (CIRBP) is a stress-responsive protein that promotes cancer development and inflammation. Critical to most CIRBP functions is its capacity to bind and post-transcriptionally modulate mRNA. However, a transcriptome-wide analysis of

CIRBP mRNA targets in cancer has not yet been performed. Here, we use an ex vivo breast cancer model to identify CIRBP targets and mechanisms. We find that CIRBP transcript levels correlate with breast cancer subtype and are an indicator of luminal A/B prognosis.

Accordingly, over-expression of CIRBP in non-tumoral MCF-10A cells promotes cell growth and clonogenicity, while depletion of CIRBP from luminal A MCF-7 cells has opposite effects.

We use RNA immunoprecipitation followed by high-throughput sequencing (RIP-Seq) to identify a set of 204 high confident CIRBP targets in MCF-7 cells. About 10% of these showed complementary changes after CIRBP manipulation in MCF-10 and MCF-7 cells, and were highly interconnected with known breast cancer . To test the potential of CIRBP- mediated regulation of these targets in breast cancer development, we focused on Cystatin

C (CST3), one of the most highly interconnected genes and a protein that displays tumor suppressive capacities. CST3 depletion restored the effects of CIRBP depletion in MCF-7 cells, indicating that CIRBP functions, at least in part, by downregulating CST3 levels. Our data provide a resource of CIRBP targets in breast cancer, and identify CST3 as a novel downstream mediator of CIRBP function.

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INTRODUCTION

RNA binding orchestrate post-transcriptional control of gene expression and are emerging as important modulators of cancer progression (Moore et al., 2018; Pereira et al.,

2017; Wurth and Gebauer, 2015; García-Cárdenas et al., 2019). Cold-inducible RNA binding protein (CIRBP, also termed CIRP and hnRNP A18) is a stress-responsive protein that partially relocates from the nucleus to the cytoplasm under diverse types of stress such as mild hypothermia, UV-irradiation, endoplasmic reticulum (ER) stress or hypoxia (Lujan et al.,

2018). In the cytoplasm, CIRBP binds to the coding sequence and 3’ UTRs of target transcripts and promotes or stabilizes mRNA levels. For example, CIRBP promotes telomere maintenance by increasing the levels of TERT mRNA (Zhang et al., 2016), and enhances the tumorigenic properties of cancer cells by promoting the stability of HIF1α and cyclin E mRNAs (Chang et al., 2016; Guo et al., 2010). However, other modes of CIRBP-mediated regulation have also been reported. For instance, CIRBP stimulates global protein synthesis by promoting 4E-BP1 phosphorylation (Chang et al., 2016; Artero-Castro et al., 2009), and has been proposed to stimulate mRNA-specific translation (Chang et al., 2016; Yang et al.,

2006), although direct evidence for the latter is still missing. In addition, CIRBP regulates circadian gene expression by promoting alternative polyadenylation (APA) of target transcripts (Morf et al., 2012; Liu et al., 2013). Under UV-irradiation, CIRBP is transiently recruited to sites of DNA damage, where it promotes repair by modulating the association of

DNA repair complexes (MRN and ATM kinase) to DNA damage sites (Chen et al., 2018).

CIRBP also stimulates the ERK pathway, an event important for bypass of replicative senescence and protection from apoptosis (Artero-Castro et al., 2009; Sakurai et al., 2006).

Intriguingly, CIRBP can augment inflammation and induce pyroptosis by a mechanism presumably unrelated to its RNA-binding capacity. Upon hemorrhagic shock and sepsis,

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CIRBP is released into the circulation where it binds to the TLR4-MD2 receptor of macrophages, leading to release of TNFα, inflammation and tissue injury (Qiang et al., 2013).

Most of the functions mentioned above are consistent with a pro-oncogenic role of CIRBP.

Accordingly, although CIRBP null mice display no obvious phenotype under normal laboratory conditions, they show attenuated hepatocarcinogenesis upon thioacetamide treatment and are less susceptible to dextran sodium sulfate-induced intestinal inflammation and tumorigenesis (Sakurai et al., 2006; Sakurai et al., 2014; Sakurai et al.,

2015; Masuda et al., 2012).

Despite the relevance of CIRBP in tumor progression, no transcriptome-wide attempts have been done to identify CIRBP targets in a cancerous context. Here we use RNA immunoprecipitation followed by high-throughput sequencing (RIP-Seq) to identify CIRBP targets. We focus on breast cancer because i) previous in vitro evidence indicated that CIRBP could modulate the tumoral properties of MDA-MB-231 breast cancer cells (Chang et al.,

2016), ii) immunohistochemistry (IHC) analysis pointed to the presence of CIRBP in the cytoplasm of cells from breast cancer patients (Artero-Castro et al., 2009), and iii) the large number of samples present in databases compared to other tumor types allows for higher statistical relevance. We first validated breast cancer as a suitable model to study CIRBP- mediated regulation. Indeed, analysis of patient samples indicated that CIRBP mRNA levels correlate with breast tumor subtype and are an indicator of prognosis for the luminal A/B subtype. Depletion of CIRBP from luminal A MCF-7 breast ductal carcinoma cells resulted in decreased proliferation and clonogenicity while, conversely, CIRBP over-expression in non- tumoral breast MCF-10A cells increased these traits. RIP-Seq from MCF-7 cells identified 204 high-confident targets, 80% of them novel. To identify functionally relevant targets, we used the MCF-7/MCF-10A cell pair and compared transcriptome changes after CIRBP depletion or

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over-expression, respectively. Of the 204 targets, 22 showed consistent changes in both cell lines. Network analysis indicated that 13 of these targets were highly interconnected with known breast cancer genes. We provide proof-of-principle analysis showing that one of these novel targets, Cystatin C (CST3) is a functionally-relevant CIRBP target in breast cancer, as depletion of CST3 abrogates the deleterious effects of CIRBP depletion. These data indicate that CST3 is a novel mediator of CIRBP function in breast cancer.

RESULTS

CIRBP is upregulated in luminal breast cancer and its expression correlates with poor clinical outcome

To assess the relevance of CIRBP in cancer, we first interrogated CIRBP expression in patient databases using cBioPortal (https://www.cbioportal.org/) (Cerami et al., 2012; Gao et al.,

2013). TCGA database analysis indicated that CIRBP is altered in a variety of human tumors, the most frequent event consisting of mRNA upregulation (Figure 1A). We next focused on breast cancer, which had the highest absolute number of CIRBP alterations. Breast cancer is classified in four main subtypes based on the expression of molecular markers, namely estrogen receptor (ER), progesterone receptor (PR) and human epithelial growth factor receptor 2 (HER2) (Harbeck et al., 2019). In increasing order of aggressiveness, tumors are low proliferative ER+PR+HER2- (luminal A), high proliferative ER+PR+HER2- (luminal B), HER2+

(luminal B and non-luminal) or ER-PR-HER2- (triple negative). Analysis of CIRBP expression along these subtypes revealed a higher transcript expression in less aggressive hormone- positive (ER+PR+) breast cancer samples present in TCGA and METABRIC databases (Figure

1B). Although less aggressive, hormone-positive breast cancer represents the most

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abundant class (60-70%), and can metastasize leading to death. To test whether CIRBP levels can function as a prognosis factor, we explored CIRBP transcript prevalence in ER+PR+ patients with primary infiltrating ductal carcinoma and negative lymph node status. The results indicated that CIRBP upregulation correlates with poor disease-free survival (Figure

1C).

In order to select an appropriate cell system to study the function and targets of CIRBP in breast cancer, we next explored CIRBP protein levels in a set of breast cancer cell lines.

CIRBP was over-expressed in luminal (MCF-7, T47D, BT474) compared with non-luminal

(MDA-MB-468 and SKBR3) and non-tumoral (MCF-10A) cells (Figure 1D). We, thus, selected the luminal MCF-7 and the non-tumoral MCF-10A cell line pair for our study.

CIRBP promotes proliferation

To evaluate the role of CIRBP in our cellular model, we both over-expressed CIRBP in MCF-

10A cells and down-regulated CIRBP in MCF-7 cells. Even mild over-expression of CIRBP (to

~30% of endogenous CIRBP levels) resulted in increased cell proliferation and clonogenicity

(Figure 2A-C), while depletion of CIRBP had the opposite effect (Figure 2D-F). These results are consistent with a pro-oncogenic role of CIRBP in breast cancer.

Transcriptome-wide identification of CIRBP targets

To identify CIRBP mRNA targets in MCF-7 cells in an unbiased, transcriptome-wide manner, we used RNA immunoprecipitation followed by high-throughput sequencing (RIP-Seq)

(Figure 3A). Given that CIRBP is located in the nucleus of MCF-7 cells, we chose a cell lysis protocol that included sonication but only mild detergents to preserve RNA-protein interactions. CIRBP was immunoprecipitated from the lysates and the associated RNA

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sequenced using poly(A) RNA-Seq. Parallel reactions with non-specific rabbit IgG were carried as negative control. The IP efficiency of duplicate experiments is shown in Figure 3B.

As expected, only a small proportion of the input RNA was associated to CIRBP (Figure S1A) and the obtained sequences are biased towards the mRNA 3’ end (Figure S1B). We believe that this bias reflects the chosen protocol for the identification of targets rather than an intrinsic property of CIRBP binding, because sonication shears the RNA and poly(A) selection then enriches for 3’ fragments. However, we cannot rule out a preferential binding of CIRBP to the 3’ UTR in MCF-7 cells, as reported in other contexts (Morf et al., 2012; Liu et al.,

2013). Sequencing of the two RIP replicates showed a high correlation (Figure S1C). We performed pairwise comparisons of CIRBP IP vs IgG or CIRBP IP vs input, considering either all counts for each gene or only counts in the last 3 exons to account for the 3’ bias (Figure

S1D and Table S1). A larger number of targets were retrieved in the CIRBP vs input comparison, while minimal differences were observed using whole gene or last exons counts

(Figure 3C and S1D). Independent RT-qPCR validation of targets selected over a wide range of p-values yielded a validation rate of 73% (Figure S1E). We reasoned that robust CIRBP targets should be enriched over both the input and the IgG control; thereby we selected a subset of 204 targets enriched under both comparisons as our high-confident target list

(Figure 3C and Table S1). Around 90% of these targets are protein-coding genes while the remaining 10% include long non-coding RNAs and pseudogenes (Figure 3C and Table S1).

Gene Ontology (GO) analysis revealed biological processes related with DNA damage and proliferation, in line with previous reports showing that CIRBP is involved in the DNA damage response (Yang et al., 2006; Chen et al., 2018; Yang and Carrier, 2001; Yang et al., 2010)

(Figure 3D). However, none of our targets had been described in previous CIRBP analyses, suggesting novel mediators for DNA damage regulation by CIRBP. We further compared our

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list with CIRBP targets defined in two previous transcriptome-wide efforts aimed at the study of circadian gene regulation in the mouse (Liu et al., 2013; Morf et al., 2012). The comparison revealed an overlap of 32 targets (15% of our list) (Figure 3E). This limited overlap might be due to the different organism (human vs mice), cell types (epithelial cells vs fibroblasts), biological contexts (cancer vs non-tumoral) and technologies (RIP vs CLIP or

PAR-CLIP) used in our study compared to previous reports. Altogether, the results indicate that we have identified novel targets of CIRBP in human luminal breast cancer cells.

CIRBP regulates a network of breast cancer genes

As mentioned above, CIRBP has been shown to post-transcriptionally regulate the levels of target transcripts. In order to identify functionally relevant CIRBP targets in breast cancer, we thereby identified changes in the transcriptomes of MCF-10A and MCF-7 cells after over- expression and depletion of CIRBP, respectively, by RNA-Seq analysis (Table S2). Most changes observed for the 204 CIRBP targets included upregulation upon CIRBP depletion in

MCF-7 cells or down-regulation in MCF-10A cells upon CIRBP over-expression, indicating a predominant role of CIRBP in mRNA down-regulation (Figure 4A, right panel). A set of 1613 transcripts changed in both cell types, of which 28 overlapped with our high-confident CIRBP target list (Figure 4A, left panel and Table S2). A close examination of the magnitude and direction of changes in these 28 targets indicated that 22 of them changed in opposite directions in both cell lines, consistent with variations of CIRBP levels (Figure 4B).

To identify which of these 22 genes was potentially relevant in breast cancer, we performed network analysis. A list of known breast cancer genes was constructed from KEGG

(https://www.genome.jp/kegg/), COSMIC (https://cancer.sanger.ac.uk/cosmic) and TCGA databases. This list was intersected with the 22 CIRBP targets using Ingenuity Pathways

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allowing for direct and up to two indirect interactions. Strikingly, 13 of the 22 targets (59%) were highly interconnected with known breast cancer genes: CST3, S100A11, MAPK11,

STK11, CDT1, HOMER3, TRIM28, RPL28, NCAPH2, SF3A2, SLC25A10, SEZ6L2 and MEIS3

(Figure 4C). Of these, TRIM28 is a known breast cancer gene (Wei et al., 2016; Czerwinska et al., 2017), while the rest are novel. Of note, four of the cancer genes present in the network

(MAP2K2, E2F1, FZD2 and WNT7B), although absent from our high-confident list of CIRBP targets, they are enriched in CIRBP IPs (Table S1). These data indicate that our stringent criteria may have resulted in loss of true positives and, together with the high percentage of

CIRBP targets included in the network, provides confidence to our selection.

CIRBP promotes oncogenic traits through regulation of CST3

To validate CIRBP regulation of select targets, we focused on three of the highly interconnected genes: CST3, SLC25A10 and TRIM28. We corroborated the RNA-Seq results obtained after CIRBP depletion and over-expression by using RT-qPCR (Figure 5A). Results were further validated by Western blot after depletion of CIRBP from MCF-7 cells (Figure

5B).

As a proof of principle, we chose CST3 for functional studies. Our aim was to test whether

CIRBP-mediated regulation of CST3 was relevant to modulate the tumorigenic properties of

MCF-7 cells. To that end, and given that CIRBP down-regulates CST3 levels, we tested whether CST3 depletion rescues the phenotype resulting from CIRBP depletion. The results showed that, while CST3 silencing alone had minor effects on MCF-7 clonogenicity, it did overcome the reduced clonogenicity resulting from CIRBP depletion (Figure 5C, see efficiency of depletion in Figure S2A). Similar results were obtained when MCF-7 cells were grown in non-adherent plates. In these conditions, MCF-7 cells grow as 3D-aggregates whose size and viability decrease after CIRBP knock-down. Concomitant depletion of CST3

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eliminates and even surpasses this effect (Figure 5D, see efficiency of depletion in Figure

S2B). These results indicate that downregulation of CST3 mRNA levels by CIRBP contributes to the oncogenic features of MCF-7 breast cancer cells.

DISCUSSION

Despite the fact that luminal tumors A and B have a better prognosis (compared to Her2+ and TN), in the last decades an increase in the number of luminal A/B patients that have recurred has been detected. Therefore, it is essential to identify new biomarkers capable of predicting which subgroup of patients may not respond adequately to therapy or recur. RNA binding proteins are gaining great attention in the cancer field for their capacity to modulate virtually all cancer hallmarks (Pereira et al., 2017). Although they constitute one of the largest protein families in the cell, understanding of their roles in disease is in its infancy

(Wurth and Gebauer, 2015; Castello et al., 2013; Gerstberger et al., 2014; Hentze et al.,

2018). Here we show that the RNA binding protein CIRBP displays pro-oncogenic properties in breast cancer, consistent with previous reports (Chang et al., 2016). Importantly, CIRBP transcript levels correlate with poor prognosis in the most frequent luminal A/B breast cancer subtype. To understand the role of CIRBP in luminal breast cancer, we perform the first genome-wide identification of CIRBP targets in cancer, and provide a repository of novel targets with potential relevance in breast oncogenesis. Among them, we identify CST3 as a functionally relevant target that is down-regulated by CIRBP to promote the tumorigenic properties of breast cancer cells.

The most significant GO terms associated with our list of CIRBP targets are related to the

DNA damage response, in agreement with the reported function of CIRBP in this biological process (Yang et al., 2006; Chen et al., 2018; Yang and Carrier, 2001; Yang et al., 2010).

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However, none of the known CIRBP targets are present in our list, indicating alternative ways in which CIRBP can regulate DNA damage. In addition, our list only minimally intersects with transcriptome-wide studies of CIRBP in other biological contexts, suggesting that the functions of CIRBP are context-specific. This is not unusual among RNA binding proteins, which can promote or suppress tumor progression depending on cell type and condition

(Pereira et al., 2017). Strikingly, although most known cases of mRNA modulation by CIRBP involve upregulation of mRNA levels, we find a dominant role of CIRBP in mRNA downregulation, as most changes after CIRBP depletion include increase in mRNA levels while the opposite is true after CIRBP over-expression. One of the downregulated targets is

CST3, encoding for Cystatin C. Cystatins are inhibitors of cysteine proteases, enzymes which play multiple roles in physiological and pathological settings. CST3 is a major inhibitor of cathepsin B, a matrix protease that promotes invasion and escape from immune recognition by degrading antigens and extracellular matrix components (Keppler, 2006). In addition to cathepsin-dependent roles, CST3 can suppress tumorigenesis by binding to the TGFβII receptor and acting as a TGFβ antagonist (Sokol et al., 2005). Curiously, a recent report found that breast tumors of CST3 knock-out mice in a breast cancer model were smaller than their wild type counterparts, suggesting a tumor-promoting role of CST3 (Završnik et al., 2017). However, in this same model CST3-/- tumors were less aggressive, as they were less capable of metastasizing to the lungs, consistent with a tumor-suppressive role. Our results are consistent with the tumor suppressive capacities of CST3, as CIRBP promotes clonogenicity and anchorage-independent growth by downregulating CST3 levels. In our model, CST3 seems to act exclusively as a CIRBP mediator, since depletion of CST3 per se has no effect on these traits. Our data are, thus, in agreement with protective roles of CST3 described previously in breast and other cancers such as head and neck, skin, lung, ovarian

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cancer and glioma (Strojan et al., 2004; Mori et al., 2016; Yu et al., 2010; Konduri et al.,

2002; Nishikawa et al., 2004).

Finally, our network analysis shows that 12 CIRBP targets (S100A11, MAPK11, STK11, CDT1,

HOMER3, TRIM28, RPL28, NCAPH2, SF3A2, SLC25A10, SEZ6L2 and MEIS3) in addition to CST3 highly intersect with breast cancer genes. One of them, TRIM28, is known to promote breast cancer metastasis and is an important indicator of disease progression (Wei et al., 2016;

Czerwińska et al., 2017; Damieni et al., 2017). However, to our knowledge, no relation with breast cancer has been reported for the remaining 11 CIRBP targets. It will be interesting to analyze the relevance of these targets for breast cancer progression in future studies.

MATERIALS AND METHODS

Constructs

The coding sequence of human CIRBP isoform 1 (172 amino acids) was cloned with a C- terminal FLAG-Tag into the MSCV-PIG (PURO-IRES-GFP) retroviral vector using the Gibson

Cloning technology (Gibson et al., 2009) using oligos GibF

(5’CTAGGCGCCGGAATTAGATCTCTCAGTGGCCGCCATGGCAT3’) and GibR (5’

TCGTTAACCTCGAGAGATCTTTACTTGTCATCGTCATCCTTGTAATCCTCGTTGTGTGTAGCGTAAC3’).

The construct was verified by sequencing.

Over-expression and depletion

For CIRBP over-expression, MCF-10A cells were incubated with viral particles containing

CIRBP-Flag-MSCV-PIG or empty vector (240.000 cells/ 2 ml viral suspension/ well of a 6-well plate), and selected with 1 µg/ml puromycin for 3 days before collecting cell extracts. The vector contains EGFP, and thereby all cells express this marker. To obtain viral particles,

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Phoenix A cells were transfected with the respective constructs, selected with 2.5 µg/ml puromycin after 24 h, the supernatant collected 2 days after puromycin addition and concentrated using Retro-X concentrator (Clontech).

Silencing experiments were performed by transient transfection of siPools (SiTOOLs Biotech) at a concentration of 3nM using Lipofectamine RNAiMax (Thermofisher Scientific) following the manufacturer’s protocol. In transfections with two different siPOOLs, 1.5 nM of each were used.

Proliferation, 3D-growth, clonogenicity and viability assays

For clonogenicity assays, cells were seeded in triplicates at 2000 cells/well (MCF-7) or 500 cells/well (MCF-10A) in 6-well plates, and reverse transfected (i.e. mixed with the transfection mix before plating) with 3 nM siRNA. After 10 days, cells were stained with 0.5% crystal violet (diluted in 25% methanol) for one hour. Plates were scanned and images quantified using FIJI (Guzmán et al., 2014). Cells were then treated with 10% acetic acid for

30 min to release the dye, and global crystal violet intensity in the supernatant measured at

OD590.

Proliferation assays were performed by seeding cells after reverse transfection of siPOOLs, followed by cell counting every day for 4-5 days in a Neubauer chamber after trypsinization.

For 3D-growth measurements, cells were seeded at 1000 cells/well in low attachment 96- well plates (Corning) and cultured for 10-14 days with transfection of siPOOLs every three days. Cell viability was measured using CellTiter-Glo (Promega) following the recommendations of the vendor.

Cell extracts and Western Blot

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Cell extracts were prepared by homogenization in RIPA buffer (25 mM Tris-HCL pH 7.6, 150 mM NaCl, 1% NP40, 0.1% SDS, 1% Sodium Deoxycholate and 1X protease inhibitor cocktail from Roche). The cell lysate was spun for 10 minutes at 13 Krpm and the supernatant was used or snap-frozen and stored at -80ºC. The following antibodies were used: anti-CIRBP

(Abcam ab94999, 1:500), anti-CST3 (Abcam ab109508, dilution 1:1000), anti-Tubulin (Sigma

T9026, 1:5000), anti-FLAG (Sigma F3615, 1:5000), anti-SLC25A10 (Sigma HPA023048, 1:500), anti-TRIM28 (Cell Signaling 4124S, 1:500).

RNA-immunoprecipitation (RIP)

Cells were washed with PBS and resuspended in lysis buffer (100mM KCl, 5mM MgCl2,

10mM Hepes pH 7.0, 0.5% NP40, 1mM DTT and RNAsin 40U/ul), incubated 4 min on ice, snap-frozen in liquid nitrogen, thawed and sonicated in a Bioruptor Diagenode at high level for 10 minutes with cycles of 30 sec ON/OFF at 4ºC. Sonicated cells were spun and the supernatant was pre-cleared with protein A-Dynabeads. On another hand, protein A-

Dynabeads were pre-blocked with 20ug of tRNA in NET buffer (50mM Tris-HCl pH 7.5,

150mM NaCl, 0.1% NP40, 1mM EDTA) for 15 min at room temperature, washed with NET, and mixed with 8 µg of either anti-CIRBP antibody or anti-rabbit IgG for 1 hour at 4ºC, washed and resuspended in 100ul of NET. Eighty µl of these beads were mixed with 8 mg cell extract, incubated for 1 hour at 4ºC, washed 4 times with 5 volumes of NET and resuspended in 100ul of wash buffer. Ten µl were used to verify the IP efficiency by Western blot. The rest was used for RNA extraction using phenol-chloroform in the presence of 1 µl glycoblue (Ambion). DNAse treatment was performed with Turbo DNA-free Kit (Ambion).

The same steps were used to isolate RNA from the input lysate.

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RIP-Seq and RNA-Seq analysis

We performed two replicates per RIP-Seq or RNA-Seq comparison. Library preparation and sequencing were performed at the CRG Genomics Facility, after assessing RNA quality with

Bioanalyzer. The quality of fastq files was assessed with FastQC. Reads were aligned to the (ENSEMBL release 86 for RIP-Seq and 88 for RNA-Seq) with STAR. The quality of the alignments (BAM files) was assessed with the “rnaseq” module of Qualimap.

For RIP-Seq, HTSEQ was used to retrieve the number of reads overlapping to each coding gene (option --stranded=reverse), first against the whole annotation, and then only against the three last exons of each transcript. The R/Bioconductor package (DESeq2) was used to calculate the pairwise differential expression of genes between experimental groups.

Reverse transcription and quantitative PCR

Total RNA was isolated using the mirVana kit (Ambion) and treated with DNase using Turbo

DNA-free (Ambion). RNA (800 ng) was reversed transcribed using the RevertAid H Minus

First Strand cDNA Synthesis Kit (Thermo Fisher) with random hexamers in a final volume of

20 μl. The resulting cDNA was amplified by Singleplex qPCR using the following Taqman probes: CST3 (4448892-Hs00969174_m1), SLC25A10 (4448892-Hs00201730_m1), TRIM28

(4448892-Hs00232212_m1). Relative gene expression was calculated by comparative ΔΔCq method according to the MIQE guidelines (Bustin et al., 2009).

For the validation shown in Figure S1E, immunoprecipitated RNA (typically 100 ng) and 1:100 of input were reverse transcribed with Superscript II using oligo(dT) and random hexamers.

The resulting cDNA was amplified using the Power SybrTM Green PCR master mix (Applied

Biosystems) with the oligonucleotides indicated in Table 1.

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Table 1. Oligonucleotides used for validation

Gene name Forward oligonucleotide Reverse oligonucleotide

CDK16 GCATCCTGTCCAACGAGGAG GTCGCTATCAAGTCGGGGTG

CLDN9 TTCGACCTTGGCCTGATGAC CCAGGTGTAGCTTGGGCTTT

CST3 GCAACGACATGTACCACAGC TTCACCCCAGCTACGATCTGC

CYFIP2 AACGTGGACCTGCTTGAAGA ACATGATGGAGGAAGGTGGA

E2F1 AGATGGTTATGGTGATCAAAGCC ATCTGAAAGTTCTCCGAAGAGTCC

HRAS GCTGACCATCCAGCTGATCC TGCTTCCGGTAGGAATCCTCT

LOXL1 TTTCCCGCTCTCTGATTCTC TCCTGGAAAAGCTTGGACAT

PKM CGCATGCAGCACCTGATAGC AGTGACTTGAGGCTCGCAC

RALY GACGACTTCTACGACAGGCTC CCGGGGTCGCTTCACAG

STK11 AGGCCAACGTGAAGAAGGAAAT CGTTGTATAACACATCCACCAGC

TRIM28 GATGACAGTGCCACCATTTG TGACAGTCCAGGTGGAAACA

4E-BP1 CACCAGCCCTTCCAGTGATG AACTGTGACTCTTCACCGCC

ATR GGCCAAAGGCAGTTGTATTG ATGACAGGAGGGAGTTGCTG

CCNE1 GGGAGCTCAAAACTGAAGCA CCAGCAAATCCAAGCTGTCT

HIF1A CACTACCACTGCCACCACTG TGGGTAGGAGATGGAGATGC

TERT CGGTGTGCACCAACATCTAC GGGTTCTTCCAAACTTGCTG

TRX TCAAATGCATGCCAACATTCCA ATGGTGGCTTCAAGCTTTTCCT

ACTB CCTTTGCCGATCCGCCGC CCATCACGCCTGGTGCC

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EIF2A TGTCACTAACAAGGGACTACTGC CTGACCAGGATGGACACCAAT

ACKNOWLEDGEMENTS

We gratefully acknowledge the CRG Bioinformatics Unit and Genomics Facility for high- throughput sequencing and analysis. We thank Cristina Mir, Alex Lyakhovich, Yoelsis García and Mileydis Pérez for advice and technical help at initial stages of this project. A.I. was supported by a CRG PhD4MD fellowship. F.G. was supported by grants from the Spanish

Ministry of Science and Innovation (MICINN, PGC2018-099697-B-I00), ‘la Caixa’ Foundation

(ID 100010434) under the agreement LCF/PR/HR17/52150016, the Catalan Agency for

Research and Universities (2017SGR534) and the Centre of Excellence Severo Ochoa. M.E.LL. was supported by grants from the Instituto de Salud Carlos III:PI15/01262 and CP03/00101 co-financed by the European Regional Fund (ERDF) and AECC Funding Ref.

GC16173720CARR.

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FIGURE LEGENDS

Figure 1. CIRBP is upregulated in luminal breast cancer and correlates with poor clinical outcome. (A) CIRBP genomic alterations (mutations, deletions and amplifications) and mRNA transcript levels were evaluated across cancer types using the TCGA database. (B)

Expression of CIRBP mRNA across breast cancer subtypes using METABRIC (left panel) and

TCGA (middle and right panels) databases. Box-plot center line represents the median, box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range, and dots represent outliers. Statistics were performed using Kruskal-Wallis (left panel) and Mann-Whitney U (middle and right panels). (C) Kaplan Meier curve for Disease

Free survival in patients with infiltrating ductal carcinoma of the Luminal A/B subtype, and negative lymph node. The optimal cut point was found using the Cut-off Finder script for R.

Differences between groups was calculated using log rank test. (D) Western blot analysis of

CIRBP levels in breast cancer cell lines. Tubulin is shown as a loading control. The molecular markers of these lines are indicated in the chart below. Quantification of the CIRBP signal corrected for tubulin is shown at the bottom.

Figure 2. CIRBP promotes tumorigenic traits. (A) MCF-10A cells were stably transduced with a retroviral vector expressing CIRBP (+), or with the empty vector (-) as control. Western blots with anti-Flag (left) or anti-CIRBP (right) antibodies are shown. These cells were tested for proliferation (B) and clonogenicity (C). (D) MCF-7 cells were transiently transfected with siRNA pools against CIRBP or with a control siRNA pool. The efficiency of depletion was assessed by Western blot at the beginning of the experiment (day 0). These cells were tested for proliferation (E) and clonogenicity (F). Error bars represent the standard deviation of

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triplicate experiments. Statistical significance was assessed by Student’s T-test (* p<0.05,

**p<0.01).

Figure 3. Identification of CIRBP targets. (A) Schematic representation of our RIP-Seq approach to identify CIRBP targets. (B) Efficiency of immunoprecipitation in duplicate experiments. Immunoprecipitations with non-specific IgG were carried in parallel as control. i, input; P, pellet; S, supernatant. (C) A target was considered positive when there was significant enrichment (L2FC>1, padj<0.01) in the CIRBP IP versus the input, or in the CIRBP

IP versus the IgG control. Targets positive in both analysis (204) were considered high- confident targets. Most of these targets are protein-coding genes (right). (D)

(GO) analysis of the high confidence targets. The top 20 biological processes were selected for representation. (E) Comparison between CIRBP high-confidence targets identified in this study and previous transcriptome-wide analysis of CIRBP targets.

Figure 4. CIRBP regulates a network of breast cancer genes. (A) Differences in the transcriptomes of MCF-10A cells over-expressing CIRBP, and MCF-7 cells depleted of CIRBP, with their respective controls were determined by RNA-Seq (experiments were performed in duplicate; threshold for significance, pval<0.05). The overlap between genes changing in both cell lines and the CIRBP high-confident target list is shown on the left. The direction of change in the high-confident target list is shown on the right. (B) Detail of the direction and magnitude of change of the 28 CIRBP targets overlapping in (A). (C) Network analysis of

CIRBP targets (see text for details). Yellow, known breast cancer gene; blue, CIRBP high- confident target; pink, CIRBP target identified in this study but excluded from the high- confident list. A few targets show two colors, indicative of their classification in two groups.

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Connections represent interactions at any reported level (i.e. protein-protein, protein- mRNA/DNA, activation); arrows represent activation while bars represent inhibition. Shapes of the nodes: vertical ellipse, trans- membrane receptor; horizontal ellipse, transcription regulation; diamond, enzyme; square, cytokine; inverted triangle, kinase; vertical rectangle,

G-protein coupled receptor; horizontal rectangle, ligand-dependent nuclear receptor; trapezoid, transporter; simple circle, other; concentric circle, group or complex. Dashed, red squares represent CIRBP targets that are highly interconnected in the network and which were chosen for independent validation.

Figure 5. CST3 mediates CIRBP regulation of cancerous traits. (A) Validation of RNA-Seq results by RT-qPCR of selected targets after manipulation of CIRBP levels in MCF-7 and MCF-

10A cells. For comparison, the RNA-Seq data is included. Error bars represent the standard deviation of at least two independent experiments. (B) Levels of targets in (A) by Western blot after depletion of CIRB from MCF-7 cells. Tubulin was used as a loading control.

Quantification of at least 4 experiments, corrected for tubulin and normalized to the siControl (dashed line) is shown on the right. (C) Clonogenic assay after depletion of CIRBP and/or CST3 from MCF-7 cells. A representative image is shown per condition on the left.

Middle and right panels show the quantification of the area of colonies and global plate intensity, respectively. Error bars represent the standard deviation from triplicates carried in parallel. Two independent biological replicates were performed. (D) Anchorage independent growth of MCF-7 cells after depletion of CIRBP and/or CST3. Representative images are shown on the left. Relative viability and size of aggregates are shown in the middle and right, respectively. Error bars represent standard deviation (n=6). Significance was determined using Student’s t-test (***pval<0.001, **pval<0.01, *pval<0.05; ns, non-significant).

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Figure S1. Quality of RNA and RIP-seq analysis. (A) Bioanalyzer images showing the quality and quantity of RNA before (input) and after (CIRBP IP) immunoprecipitation. (B) Coverage profiles of the input, CIRBP IP and IgG IP sequencing reactions after poly(A) selection. The dashed line represents the coverage of one of the inputs using total RNA-Seq. (C) Correlation plot of the two RIP-Seq replicates. (D) Analysis of significant CIRBP targets using DESeq2 on the full count annotation of genes or only the last 3 exons. Comparisons of IP versus input or

IP versus IgG are shown. (E) Validation of RIP-Seq data by RT-qPCR. Blue, CIRBP targets described in this manuscript; green, CIRBP targets described in previous reports; grey, non- targets. Values represent the average of three replicates from two independent experiments, except for ACTB and EIF2A that correspond to the average of two technical replicates with similar values. Error bars represent standard deviation. Significant differences with LOXL1 was measured using Student's t-test (**pval<0.01, *pval<0.05).

Figure S2. Efficiency of depletion assessed by Western blot (related to Figure 5). (A)

Related to Figure 5C. Depletion was assessed after 2 days. (B) Related to Figure 5D.

Depletion was assessed at 6, 10 and 13 days with similar results. The image at 13 days is shown.

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Cold-inducible RNA binding protein promotes breast cancer cell malignancy by regulating Cystatin C levels

Alberto Indacochea, Santiago Guerrero, Macarena Ureña, et al.

RNA published online November 10, 2020

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